An important component for sample-based learning of dynamical models is the choice of priors, which usually comes from our understanding of the underlying physical laws [1]}, [2]}. For example, the priors can be a prior distribution over the model parameters [3]}, [4]} or a set of differential equations with undetermined physical parameters [5]}, [6]}. Here, we consider another type of priors, which are primitive geometric motions of dynamical system. These priors reflect the manifold structure of the state space of dynamical system, as explained below.
